KAN–U‐Net: An Enhanced KAN‐Fused U‐Net for Medical Image Segmentation

The U‐Net model has demonstrated strong performance in the field of medical image segmentation. Moreover, several enhanced and improved versions of this model have emerged by incorporating transformer or MLP modules. However, these network models still face challenges in overcoming the limitations of linear modeling and the lack of interpretability. Based on the excellent performance of Kolmogorov–Arnold network (KAN) in terms of accuracy and interpretability, we propose a new architecture called KAN–U‐Net. This architecture fuses the KAN network module into the U‐Net model, allowing KAN–U‐Net to inherit the original performance of U‐Net while also fusing the nonlinear representation ability and interpretability of KAN networks. The experiments on multiple datasets demonstrate that the KAN–U‐Net model outperforms in terms of precision and accuracy.